Modern methods of quantum mechanics have proved to be effective tools to understand and even predict materials properties. An essential element of the materials design process, relevant to both new materials and the optimization of existing ones, is knowing which crystal structures will form in an alloy system. Crystal structure can only be predicted effectively with quantum mechanics if an algorithm to direct the search through the large space of possible structures is found. We present a new approach to the prediction of structure that rigorously mines correlations embodied within experimental data and uses them to direct quantum mechanical techniques efficiently towards the stable crystal structure of materials.

Using density functional theory (DFT), we have investigated the mobility and insertion potential of Li on single-walled TiS2 nanotubes as a function of radius. To explore large radii, the nanotube surface is modeled as a curved surface. For small tubes, for which DFT calculations are practical on the complete tube, we find that strain energies, voltages, and activation barriers calculated with the curved surface method are nearly the same as those calculated with a full nanotube. Our results show that, for the range of nanotube radii that are seen experimentally (approximately 50−250 Å), Li diffusion on the nanotube surface is very fast and similar to diffusion on a flat surface. In general, the activation barrier for Li diffusion is 200 meV smaller on the surface than in the bulk, which could result in an improved mobility of Li by a factor of 3000 at room temperature. The effect of tube radius on the Li insertion voltage and migration energy can be explained by the electrostatic repulsion between Li and Ti and by the relaxation of the S atoms.

Multiwalled inorganic nanotubes with circular cross sections must have either an incoherent interface or a large amount of strain. However, nanotubes with a polygonal cross section can have a coherent interface with considerably less strain. We present a model for polygonal nanotubes with no defects where the chirality of the nanotube determines the shape of the cross section. Circular and polygonal nanotubes are compared based on their strain energy and interfacial energy. We have used first-principles calcuations to parameterize strain and interfacial energy for TiS 2 nanotubes. These calculations show that the polygonal model is energetically favorable to the circular model when the inner radius is above a critical radius, 6.2 Å for a TiS 2 nanotube with ten layers. These results should provide insight into further investigations of nanotube structure and allow computational studies to more accurately predict nanotube properties.

Over the last 40 years, ab initio methods have become ubiquitous tools in chemistry, physics, and materials science. Ab initio methods, which accurately solve the fundamental quantum mechanical equations (Schrödinger or Dirac) for the electrons of a system, hold the promise of virtual materials research, that is, learning the properties of materials completely by computation, before experimental synthesis and testing. In the last decade, significant advances in solid-state physics, fundamental materials science, and advanced computing have brought us closer to that objective, and accurate ab initio approaches now exist for many properties (e.g., diffusion, thermodynamic quantities, ferroelectricity, lattice parameters, elastic constants, etc.). The September 2006 issue of MRS Bulletin on density functional theory (guest-edited by J. Hafner, C. Wolverton, and G. Ceder) highlights some of the successes of ab initio methods in a variety of materials research areas.Ab initio studies are still primarily used to further the understanding and rationalize the properties of well-known materials. Studies of this type bypass the problem of predicting the structure of a material, as it is usually known from experiment. If we peek into the future and imagine true virtual materials design, our efforts will need to extend beyond property prediction and address the problem of structure prediction. Most materials properties, from bandgaps to brittle fracture, melting temperature to magnetism, depend strongly on the structure of the materials involved, and without knowledge of the crystal structure, ab initio computations easily become irrelevant. Hence, the full power of ab initio calculations for materials design will only be unlocked if we address the problem of structure prediction. In this article, we focus on equilibrium crystal structure prediction, setting aside the even more difficult problem of predicting amorphous and metastable structures. Data Mining Structure PredictionPredicting the stable crystal structure of a material in essence requires one to find the atomic arrangement with lowest free energy (at non-zero temperature), or with lowest energy (at zero temperature and pressure). We only focus here on the zerotemperature, zero-pressure ground-state search, as it is also a key component of any finite-temperature, finite-pressure study. Models to construct the free energy of a given structure or class of structures (e.g., those on a fixed topology) are well developed and have led to a large number of successful phase diagram computations. [1][2][3][4][5] Both an accurate description of the energetics of a material as well as a strategy to search through the almost infinite space of possible structures are needed to find the most stable structure. Decades of work with ab initio methods studying specific systems and/or properties, typically using the local density approximation (LDA) or generalized gradient approximation (GGA) to density functional theory (DFT), as well as a more recent large-scale comparison of compu...

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